Maximum likelihood is equivalent to Bayesian maximum a-posteriori (MAP) given a uniform prior and the right choice of parameters. However, decision theory tell

Maximum likelihood is equivalent to Bayesian maximum a-posteriori (MAP) given a uniform prior and the right choice of parameters. However, decision theory tells us, that MAP estimation is rarely, if ever, a good point estimator. The mean or the median of the posterior distribution is usually better, but the optimal choice may be something entirely different. (Why even consider MAP when it's dumb? Only because it's computationally cheaper than anything else.)